A method for maneuvering target state estimation under intermittent observation conditions

Abstract

An embodiment of the present invention discloses a method for estimating the state of a maneuvering target under intermittent observation conditions. In a specific embodiment, the method includes initializing the estimated values ​​and covariance matrix of a network node; calculating a correlation matrix for the network node; calculating a weight matrix based on the correlation matrix; and performing information fusion based on the weight matrix to obtain an updated estimated value and covariance matrix. This embodiment compensates for the problem of insufficient observations under intermittent observation conditions by utilizing the original observation information of neighboring nodes. The weight matrix is ​​calculated in real time during the fusion stage, and the fusion weight is dynamically adjusted according to the estimation accuracy of the neighboring nodes to achieve effective estimation of highly maneuverable targets.

Description

Technical Field

[0001] The present invention relates to the field of distributed filtering algorithms, and more particularly to a method for estimating the state of a maneuvering target under intermittent observation conditions. Background Art

[0002] Due to factors such as signal interference and fluctuations and the limited detection range of sensors, in actual combat, it is difficult for sensors to obtain observations of targets in each observation cycle, or the information obtained by the sensors cannot be transmitted to the information processing node due to obstruction of the transmission channel.

[0003] Intermittent observations are a phenomenon in which observations cannot be updated at every observation moment. In this case, the estimated value cannot be corrected based on the observed value, which can easily lead to filter degradation or even divergence. Reliably estimating the target under intermittent observations has become a pressing technical challenge. Existing distributed filtering methods have certain shortcomings in estimation accuracy and reliability. Summary of the Invention

[0004] The object of the present invention is to provide a method, computer equipment and medium for estimating the state of a maneuvering target under intermittent observation conditions, so as to solve at least one of the problems existing in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A first aspect of the present invention provides a method for estimating the state of a maneuvering target under intermittent observation conditions, the method comprising:

[0007] Initialize the estimated values ​​and covariance matrix of the network nodes;

[0008] Performing correlation matrix calculation on the network nodes;

[0009] Calculating a weight matrix based on the correlation matrix;

[0010] Information fusion is performed according to the weight matrix to obtain updated estimated values ​​and covariance matrices.

[0011] Optionally, the calculation formula of the correlation matrix of node i at time k is

[0012]

[0013] Where, is the prior estimate of node i at time k; is the first intermediate state quantity of node i at time k; is the second intermediate state quantity of node i at time k; is the third intermediate state quantity of node i at time k.

[0014] Optionally, the calculation formula for the first intermediate state quantity of the node i at time k is:

[0015]

[0016] Where, is the prior covariance matrix of node i at time k; is the third intermediate state quantity of node i at time k.

[0017] Optionally, the calculation formula for the second intermediate state quantity of the node i at time k is:

[0018]

[0019] Where N i is the set of adjacent nodes of node i; is the intermittent observation coefficient of node j at time k; is the Jacobian matrix of node j at time k; is the observation noise covariance matrix of node j at time k; is the observation value of node j at time k.

[0020] Optionally, the calculation formula for the third intermediate state quantity of the node i at time k is:

[0021]

[0022] Where N i is the set of adjacent nodes of node i; is the intermittent observation coefficient of node j at time k; is the Jacobian matrix of node j at time k; is the observation noise covariance matrix of node j at time k.

[0023] Optionally, calculating the weight matrix according to the correlation matrix includes calculating a fusion matrix; wherein,

[0024] The calculation formula for the (i, j)th element in the calculation fusion matrix is:

[0025]

[0026] Where N i is the set of adjacent nodes of node i; A i,j is the (i, j)th element of the adjacency matrix A of node i; is the first intermediate state quantity of node j at time k; Tr(*) represents the trace of the matrix.

[0027] Optionally, performing information fusion according to the weight matrix includes:

[0028] The covariance matrix of the network node is obtained using the information of the neighboring nodes; wherein, the calculation formula for obtaining the covariance matrix of node i at time k is:

[0029]

[0030] Where N i is the set of adjacent nodes of node i; i,j is the (i, j)th element of the fusion matrix; is the first intermediate state quantity of node j at time k.

[0031] Optionally, the information fusion according to the weight matrix further includes:

[0032] The estimated value of the network node is obtained by using the information of the neighboring nodes; wherein the calculation formula for obtaining the estimated value of node i at time k is:

[0033]

[0034] Where, is the covariance matrix of node i at time k; N i is the set of adjacent nodes of node i; i,j is the (i, j)th element of the fusion matrix; is the first intermediate state quantity of node j at time k; is the correlation matrix of node j at time k.

[0035] A second aspect of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for estimating the state of a maneuvering target under intermittent observation conditions is implemented.

[0036] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements a method for estimating the state of a maneuvering target under intermittent observation conditions.

[0037] The beneficial effects of the present invention are as follows:

[0038] Aiming at the state estimation of highly maneuverable targets under intermittent observation conditions, the present invention proposes a method for state estimation of maneuverable targets under intermittent observation conditions. By utilizing the original observation information of neighboring nodes, the problem of insufficient observation quantity under intermittent observation conditions is compensated. The weight matrix is ​​calculated in real time in the fusion stage, and the fusion weight is dynamically adjusted according to the estimation accuracy of the neighboring nodes to complete the effective estimation of highly maneuverable targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0040] Figure 1 A flow chart of a method for estimating the state of a maneuvering target under intermittent observation conditions provided by an embodiment of the present invention is shown.

[0041] Figure 2 A schematic diagram showing the structure of a computer system for implementing the method provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0042] In order to more clearly illustrate the present invention, the present invention will be further described below in conjunction with the embodiments and drawings. Similar components in the drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the following specific description is illustrative rather than restrictive and should not be used to limit the scope of protection of the present invention.

[0043] Intermittent observations are a phenomenon in which observations cannot be updated at every observation moment. In this case, the estimated value cannot be corrected based on the observed value, which can easily lead to filter degradation or even divergence. Reliably estimating the target under intermittent observations has become a pressing technical challenge. Existing distributed filtering methods have certain shortcomings in estimation accuracy and reliability.

[0044] In view of this, an embodiment of the present invention provides a method for estimating the state of a maneuvering target under intermittent observation conditions, the method comprising initializing the estimated value and covariance matrix of the network node; calculating the correlation matrix of the network node; calculating the weight matrix based on the correlation matrix; performing information fusion based on the weight matrix to obtain an updated estimated value and covariance matrix.

[0045] Specifically, the specific steps of a method for estimating the state of a high-maneuverability target under intermittent observation conditions are as follows:

[0046] S1: Network node initialization

[0047] Initialize the estimated value and covariance matrix of each node.

[0048] Furthermore, the estimated value and covariance matrix of each node i at the initial moment are initialized to obtain and

[0049] S2: Calculate the correlation matrix at each node

[0050] Information fusion is performed based on the framework of EKF (Extended Kalman Filter algorithm), and the correlation matrix is ​​first calculated.

[0051] S3: Calculate the weight matrix

[0052] Calculate the weight matrix based on the calculated covariance matrix.

[0053] S4: Information fusion

[0054] The information of neighboring nodes is fused to obtain the updated estimated value and covariance matrix.

[0055] This embodiment models intermittent observations in sensor networks, calculates the information required to be transmitted between nodes based on the EKF algorithm, and updates the fusion weights in real time using the trace of the covariance matrix. By leveraging the original observation information of neighboring nodes, this embodiment achieves high estimation accuracy even under intermittent observation conditions.

[0056] In one possible implementation, the calculation formula of the correlation matrix of node i at time k is:

[0057]

[0058] Where, is the prior estimate of node i at time k; is the first intermediate state quantity of node j at time k; is the second intermediate state quantity of node i at time k; is the third intermediate state quantity of node i at time k.

[0059] In a possible implementation, the calculation formula for the first intermediate state quantity of the node i at time k is:

[0060]

[0061] Where, is the prior covariance matrix of node i at time k; is the third intermediate state quantity of node i at time k.

[0062] In a possible implementation, the calculation formula for the second intermediate state quantity of the node i at time k is:

[0063]

[0064] Where N i is the set of adjacent nodes of node i; is the intermittent observation coefficient of node j at time k; is the Jacobian matrix of node j at time k; is the observation noise covariance matrix of node j at time k; is the observation value of node j at time k.

[0065] In a possible implementation, the calculation formula of the third intermediate state quantity of the node i at time k is:

[0066]

[0067] Where N i is the set of adjacent nodes of node i; is the intermittent observation coefficient of node j at time k; is the Jacobian matrix of node j at time k; is the observation noise covariance matrix of node j at time k.

[0068] Specifically, the Jacobian matrix of the system state equation at time k-1 is calculated as

[0069]

[0070] Where f is the nonlinear state equation, x is the state variable, is the Jacobian matrix of node i at time k-1.

[0071] Calculate the prior estimate and covariance matrix as

[0072]

[0073]

[0074] in, is the prior estimate at time k, is the prior covariance matrix at time k, is the posterior covariance matrix at time k-1, Q k-1 is the system process noise covariance matrix.

[0075] The Jacobian matrix of the observation equation is calculated as

[0076]

[0077] Among them, h i is the nonlinear observation equation of node i, is the Jacobian matrix of node i at time k.

[0078] The matrix that needs to be transferred between computing nodes is

[0079]

[0080]

[0081]

[0082]

[0083] Among them, N i represents the set of adjacent nodes of node i, represents the observation value of node j, represents the observation noise covariance matrix of node j, Characterizing intermittent observations, It means that the observation is available. Indicates that the observation is lost.

[0084] In a possible implementation, calculating the weight matrix according to the correlation matrix includes calculating a fusion matrix; wherein,

[0085] The calculation formula for the (i, j)th element in the calculation fusion matrix is:

[0086]

[0087] Where N i is the set of adjacent nodes of node i; A i,j is the (i, j)th element of the adjacency matrix A of node i; is the first intermediate state quantity of node j at time k; Tr(*) represents the trace of the matrix.

[0088] Specifically, calculate the fusion matrix Π. The (i, j)th element of Π is

[0089]

[0090] Among them, A i,j Represents the (i, j)th element of the adjacency matrix A of node i. If nodes i and j can communicate with each other, then A i,j =1, otherwise A i,j =0, special, A i,i = 1. Tr(*) represents the trace of the matrix.

[0091] In one possible implementation, the information fusion according to the weight matrix includes obtaining the covariance matrix of the network node using the information of the neighboring nodes; wherein the calculation formula for obtaining the covariance matrix of node i at time k is:

[0092]

[0093] Where N i is the set of adjacent nodes of node i; i,j is the (i, j)th element of the fusion matrix; is the first intermediate state quantity of node j at time k.

[0094] In a possible implementation, the information fusion according to the weight matrix further includes obtaining an estimated value of the network node using information of neighboring nodes; wherein the calculation formula for obtaining the estimated value of node i at time k is:

[0095]

[0096] Where, is the covariance matrix of node i at time k; N i is the set of adjacent nodes of node i; i,j is the (i, j)th element of the fusion matrix; is the first intermediate state quantity of node j at time k; is the correlation matrix of node j at time k.

[0097] Specifically, it transmits its own information to neighboring nodes, waits for the arrival of neighboring node information, and uses the information of neighboring nodes to obtain its own estimated value. and the covariance matrix They are

[0098]

[0099]

[0100] in, Calculation methods for the two intermediate quantities have been given in the present invention.

[0101] This embodiment provides a distributed filtering method under intermittent observation conditions, which fully utilizes the original observation information of neighboring nodes to make up for the lack of its own observation information. In the fusion stage, an adaptive method is used to dynamically adjust the fusion weights to achieve effective estimation of highly maneuverable targets.

[0102] like Figure 2 As shown, a computer system suitable for implementing the method for estimating the state of a maneuvering target under intermittent observation conditions provided by the above-mentioned embodiment includes a central processing module (CPU), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM). Various programs and data required for the operation of the computer system are also stored in the RAM. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0103] The following components are connected to the I / O interface: an input section including a keyboard, mouse, and the like; an output section including a liquid crystal display (LCD) and speakers; a storage section including a hard disk; and a communication section including a network interface card such as a LAN card or modem. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. Removable media such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, and the like are installed in the drive as needed, so that computer programs read from the media can be installed in the storage section as needed.

[0104] In particular, according to this embodiment, the process described in the flowchart above can be implemented as a computer software program. For example, this embodiment includes a computer program product comprising a computer program tangibly embodied on a computer-readable medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component and / or installed from a removable medium.

[0105] The flowcharts and schematic diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the system, method and computer program product of the present embodiment. In this regard, each box in the flowchart or schematic diagram can represent a module, program segment or part of code, and the part of the above-mentioned module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the schematic diagram and / or flowchart, and the combination of boxes in the schematic diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0106] The modules described in this embodiment may be implemented in software or hardware. The modules may also be provided in a processor. For example, the processor may be described as comprising an initialization module, a correlation matrix calculation module, a weight matrix calculation module, and an information fusion module.

[0107] As another aspect, this embodiment further provides a non-volatile computer storage medium. This non-volatile computer storage medium may be the non-volatile computer storage medium included in the apparatus described in the above embodiment, or may be a separate non-volatile computer storage medium not incorporated into a terminal. The non-volatile computer storage medium stores one or more programs. When executed by a device, the device utilizes raw observation information from neighboring nodes to compensate for insufficient observations under intermittent observation conditions. The device calculates a weight matrix in real time during the fusion phase, dynamically adjusts fusion weights based on the estimated accuracy of neighboring nodes, and effectively estimates highly maneuverable targets.

[0108] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper" and "lower" is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be internal communication between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0109] It should also be noted that, in the description of the present invention, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0110] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for estimating the state of a maneuvering target under intermittent observation conditions, characterized in that: The method includes Initialize the estimated values ​​and covariance matrix of the network nodes; Performing correlation matrix calculation on the network nodes; Calculating a weight matrix based on the correlation matrix; Perform information fusion according to the weight matrix to obtain updated estimated values ​​and covariance matrices; The calculation formula of the correlation matrix of node i at time k is: Where, is the prior estimate of node i at time k; is the first intermediate state quantity of node i at time k; is the second intermediate state quantity of node i at time k; is the third intermediate state quantity of node i at time k; The calculation formula of the first intermediate state quantity of node i at time k is: Where, is the prior covariance matrix of node i at time k; is the third intermediate state quantity of node i at time k; The calculation formula of the second intermediate state quantity of node i at time k is: Where N i is the set of adjacent nodes of node i; is the intermittent observation coefficient of node j at time k; is the Jacobian matrix of node j at time k; is the observation noise covariance matrix of node j at time k; is the observation value of node j at time k; The calculation formula of the third intermediate state quantity of node i at time k is: Where N i is the set of adjacent nodes of node i; is the intermittent observation coefficient of node j at time k; is the Jacobian matrix of node j at time k; is the observation noise covariance matrix of node j at time k; The calculation of the weight matrix according to the correlation matrix includes calculating a fusion matrix; wherein, The calculation formula for the (i, j)th element in the calculation fusion matrix is: Where N i is the set of adjacent nodes of node i; A i,j is the (i, j)th element of the adjacency matrix A of node i; is the first intermediate state quantity of node i at time k; Tr(*) represents the trace of the matrix; The information fusion according to the weight matrix includes The covariance matrix of the network node is obtained using the information of the neighboring nodes; wherein, the calculation formula for obtaining the covariance matrix of node i at time k is: Where N i is the set of adjacent nodes of node i; i,j is the (i, j)th element of the fusion matrix; is the first intermediate state quantity of node j at time k; The information fusion according to the weight matrix also includes The estimated value of the network node is obtained by using the information of the neighboring nodes; wherein the calculation formula for obtaining the estimated value of node i at time k is: Where, is the covariance matrix of node i at time k; N i is the set of adjacent nodes of node i; i,j is the (i, j)th element of the fusion matrix; is the first intermediate state quantity of node j at time k; is the correlation matrix of node j at time k.

2. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to claim 1 is implemented.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 1 is implemented.

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